



import argparse
import re

import numpy as np
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import (
    SamConfig,
    SamImageProcessor,
    SamModel,
    SamProcessor,
    SamVisionConfig,
)


a      :  Dict			     =			{
    'iou_prediction_head.layers.0': 'iou_prediction_head.proj_in',
    'iou_prediction_head.layers.1': 'iou_prediction_head.layers.0',
    'iou_prediction_head.layers.2': 'iou_prediction_head.proj_out',
    'mask_decoder.output_upscaling.0': 'mask_decoder.upscale_conv1',
    'mask_decoder.output_upscaling.1': 'mask_decoder.upscale_layer_norm',
    'mask_decoder.output_upscaling.3': 'mask_decoder.upscale_conv2',
    'mask_downscaling.0': 'mask_embed.conv1',
    'mask_downscaling.1': 'mask_embed.layer_norm1',
    'mask_downscaling.3': 'mask_embed.conv2',
    'mask_downscaling.4': 'mask_embed.layer_norm2',
    'mask_downscaling.6': 'mask_embed.conv3',
    'point_embeddings': 'point_embed',
    'pe_layer.positional_encoding_gaussian_matrix': 'shared_embedding.positional_embedding',
    'image_encoder': 'vision_encoder',
    'neck.0': 'neck.conv1',
    'neck.1': 'neck.layer_norm1',
    'neck.2': 'neck.conv2',
    'neck.3': 'neck.layer_norm2',
    'patch_embed.proj': 'patch_embed.projection',
    '.norm': '.layer_norm',
    'blocks': 'layers',
}







def  lowerCAmelCase_	(lowerCAmelCase__:		Tuple  ):





			"""simple docstring"""


			UpperCAmelCase_:		Tuple							      =     {}
			state_dict.pop("""pixel_mean"""		,						lowerCAmelCase__  )
			state_dict.pop("""pixel_std"""		,						lowerCAmelCase__  )

			UpperCAmelCase_:		Tuple							      =     r""".*.output_hypernetworks_mlps.(\d+).layers.(\d+).*"""

			for key, value in state_dict.items():
						for key_to_modify, new_key in KEYS_TO_MODIFY_MAPPING.items():
									if key_to_modify in key:
												UpperCAmelCase_:		Dict							      =     key.replace(lowerCAmelCase__		,						lowerCAmelCase__  )

						if re.match(lowerCAmelCase__		,						lowerCAmelCase__  ):
									UpperCAmelCase_:		int							      =     int(re.match(lowerCAmelCase__		,						lowerCAmelCase__  ).group(2  )  )
									if layer_nb == 0:
												UpperCAmelCase_:		List[str]							      =     key.replace("""layers.0"""		,						"""proj_in"""  )
									elif layer_nb == 1:
												UpperCAmelCase_:		Tuple							      =     key.replace("""layers.1"""		,						"""layers.0"""  )
									elif layer_nb == 2:
												UpperCAmelCase_:		Dict							      =     key.replace("""layers.2"""		,						"""proj_out"""  )

						UpperCAmelCase_:		List[str]							      =     value

			UpperCAmelCase_:		Dict							      =     model_state_dict[
			    """prompt_encoder.shared_embedding.positional_embedding"""
			]

			return model_state_dict







def  lowerCAmelCase_	(lowerCAmelCase__:		int		,						lowerCAmelCase__:		int		,						lowerCAmelCase__:		Optional[Any]		,						lowerCAmelCase__:		Dict="ybelkada/segment-anything"  ):





			"""simple docstring"""


			UpperCAmelCase_:		Tuple							      =     hf_hub_download(lowerCAmelCase__		,						F'checkpoints/{model_name}.pth'  )

			if "sam_vit_b" in model_name:
						UpperCAmelCase_:		Any							      =     SamConfig()
			elif "sam_vit_l" in model_name:
						UpperCAmelCase_:		List[Any]							      =     SamVisionConfig(
						    hidden_size=1_0_2_4		,						num_hidden_layers=2_4		,						num_attention_heads=1_6		,						global_attn_indexes=[5, 1_1, 1_7, 2_3]		,						)

						UpperCAmelCase_:		List[str]							      =     SamConfig(
						    vision_config=lowerCAmelCase__		,						)
			elif "sam_vit_h" in model_name:
						UpperCAmelCase_:		int							      =     SamVisionConfig(
						    hidden_size=1_2_8_0		,						num_hidden_layers=3_2		,						num_attention_heads=1_6		,						global_attn_indexes=[7, 1_5, 2_3, 3_1]		,						)

						UpperCAmelCase_:		Dict							      =     SamConfig(
						    vision_config=lowerCAmelCase__		,						)

			UpperCAmelCase_:		Any							      =     torch.load(lowerCAmelCase__		,						map_location="""cpu"""  )
			UpperCAmelCase_:		int							      =     replace_keys(lowerCAmelCase__  )

			UpperCAmelCase_:		Tuple							      =     SamImageProcessor()

			UpperCAmelCase_:		List[Any]							      =     SamProcessor(image_processor=lowerCAmelCase__  )
			UpperCAmelCase_:		Tuple							      =     SamModel(lowerCAmelCase__  )

			hf_model.load_state_dict(lowerCAmelCase__  )
			UpperCAmelCase_:		int							      =     hf_model.to("""cuda"""  )

			UpperCAmelCase_:		Tuple							      =     """https://huggingface.co/ybelkada/segment-anything/resolve/main/assets/car.png"""
			UpperCAmelCase_:		List[str]							      =     Image.open(requests.get(lowerCAmelCase__		,						stream=lowerCAmelCase__  ).raw  ).convert("""RGB"""  )

			UpperCAmelCase_:		Optional[int]							      =     [[[4_0_0, 6_5_0]]]
			UpperCAmelCase_:		Tuple							      =     [[1]]

			UpperCAmelCase_:		Optional[Any]							      =     processor(images=np.array(lowerCAmelCase__  )		,						return_tensors="""pt"""  ).to("""cuda"""  )

			with torch.no_grad():
						UpperCAmelCase_:		str							      =     hf_model(**lowerCAmelCase__  )
			UpperCAmelCase_:		Optional[int]							      =     output.iou_scores.squeeze()

			if model_name == "sam_vit_h_4b8939":
						assert scores[-1].item() == 0.579890251159668

						UpperCAmelCase_:		str							      =     processor(
						    images=np.array(lowerCAmelCase__  )		,						input_points=lowerCAmelCase__		,						input_labels=lowerCAmelCase__		,						return_tensors="""pt"""  ).to("""cuda"""  )

						with torch.no_grad():
									UpperCAmelCase_:		Dict							      =     hf_model(**lowerCAmelCase__  )
						UpperCAmelCase_:		Optional[int]							      =     output.iou_scores.squeeze()

						assert scores[-1].item() == 0.9712603092193604

						UpperCAmelCase_:		List[str]							      =     ((7_5, 2_7_5, 1_7_2_5, 8_5_0),)

						UpperCAmelCase_:		List[str]							      =     processor(images=np.array(lowerCAmelCase__  )		,						input_boxes=lowerCAmelCase__		,						return_tensors="""pt"""  ).to("""cuda"""  )

						with torch.no_grad():
									UpperCAmelCase_:		int							      =     hf_model(**lowerCAmelCase__  )
						UpperCAmelCase_:		int							      =     output.iou_scores.squeeze()

						assert scores[-1].item() == 0.8686015605926514

						# Test with 2 points and 1 image.
						UpperCAmelCase_:		Optional[int]							      =     [[[4_0_0, 6_5_0], [8_0_0, 6_5_0]]]
						UpperCAmelCase_:		List[str]							      =     [[1, 1]]

						UpperCAmelCase_:		Optional[int]							      =     processor(
						    images=np.array(lowerCAmelCase__  )		,						input_points=lowerCAmelCase__		,						input_labels=lowerCAmelCase__		,						return_tensors="""pt"""  ).to("""cuda"""  )

						with torch.no_grad():
									UpperCAmelCase_:		Any							      =     hf_model(**lowerCAmelCase__  )
						UpperCAmelCase_:		List[Any]							      =     output.iou_scores.squeeze()

						assert scores[-1].item() == 0.9936047792434692


if __name__ == "__main__":
	a      :  List[Any]			     =			argparse.ArgumentParser()
	a      :  List[str]			     =			['sam_vit_b_01ec64', 'sam_vit_h_4b8939', 'sam_vit_l_0b3195']
	parser.add_argument(
	    '--model_name',
	    default='sam_vit_h_4b8939',
	    choices=choices,
	    type=str,
	    help='Path to hf config.json of model to convert',
	)
	parser.add_argument('--pytorch_dump_folder_path', default=None, type=str, help='Path to the output PyTorch model.')
	parser.add_argument(
	    '--push_to_hub',
	    action='store_true',
	    help='Whether to push the model and processor to the hub after converting',
	)
	parser.add_argument(
	    '--model_hub_id',
	    default='ybelkada/segment-anything',
	    choices=choices,
	    type=str,
	    help='Path to hf config.json of model to convert',
	)

	a      :  List[Any]			     =			parser.parse_args()

	convert_sam_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub, args.model_hub_id)











import gc
import random
import unittest

import numpy as np
import torch
from transformers import XLMRobertaTokenizer

from diffusers import (
    AltDiffusionImgaImgPipeline,
    AutoencoderKL,
    PNDMScheduler,
    UNetaDConditionModel,
)
from diffusers.image_processor import VaeImageProcessor
from diffusers.pipelines.alt_diffusion.modeling_roberta_series import (
    RobertaSeriesConfig,
    RobertaSeriesModelWithTransformation,
)
from diffusers.utils import floats_tensor, load_image, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu


enable_full_determinism()







class 				_a				(   unittest.TestCase					):

			def      __snake_case       (self					)       ->   Optional[Any]:
						# clean up the VRAM after each test
						super().tearDown()
						gc.collect()
						torch.cuda.empty_cache()

			@property
			def      __snake_case       (self					)       ->   int:
						UpperCAmelCase_:		str							      =     1
						UpperCAmelCase_:		Optional[int]							      =     3
						UpperCAmelCase_:		Optional[Any]							      =     (32, 32)

						UpperCAmelCase_:		int							      =     floats_tensor((batch_size, num_channels) + sizes,   rng=random.Random(0					)					).to(SCREAMING_SNAKE_CASE_					)
						return image

			@property
			def      __snake_case       (self					)       ->   str:
						torch.manual_seed(0					)
						UpperCAmelCase_:		Optional[int]							      =     UNetaDConditionModel(
						    block_out_channels=(32, 64),   layers_per_block=2,   sample_size=32,   in_channels=4,   out_channels=4,   down_block_types=("""DownBlock2D""", """CrossAttnDownBlock2D"""),   up_block_types=("""CrossAttnUpBlock2D""", """UpBlock2D"""),   cross_attention_dim=32,   )
						return model

			@property
			def      __snake_case       (self					)       ->   Optional[int]:
						torch.manual_seed(0					)
						UpperCAmelCase_:		Union[str, Any]							      =     AutoencoderKL(
						    block_out_channels=[32, 64],   in_channels=3,   out_channels=3,   down_block_types=["""DownEncoderBlock2D""", """DownEncoderBlock2D"""],   up_block_types=["""UpDecoderBlock2D""", """UpDecoderBlock2D"""],   latent_channels=4,   )
						return model

			@property
			def      __snake_case       (self					)       ->   Tuple:
						torch.manual_seed(0					)
						UpperCAmelCase_:		List[Any]							      =     RobertaSeriesConfig(
						    hidden_size=32,   project_dim=32,   intermediate_size=37,   layer_norm_eps=1E-05,   num_attention_heads=4,   num_hidden_layers=5,   pad_token_id=1,   vocab_size=5006,   )
						return RobertaSeriesModelWithTransformation(SCREAMING_SNAKE_CASE_					)

			@property
			def      __snake_case       (self					)       ->   Union[str, Any]:
						def extract(*SCREAMING_SNAKE_CASE_,   **SCREAMING_SNAKE_CASE_					):







									class 				_a				:

												def __init__(self					)       ->   Tuple:
															UpperCAmelCase_:		Any							      =     torch.ones([0]					)

												def      __snake_case       (self,   SCREAMING_SNAKE_CASE_					)       ->   List[Any]:
															self.pixel_values.to(SCREAMING_SNAKE_CASE_					)
															return self






									return Out()

						return extract

			def      __snake_case       (self					)       ->   int:
						UpperCAmelCase_:		List[str]							      =     """cpu"""  # ensure determinism for the device-dependent torch.Generator
						UpperCAmelCase_:		Union[str, Any]							      =     self.dummy_cond_unet
						UpperCAmelCase_:		Any							      =     PNDMScheduler(skip_prk_steps=SCREAMING_SNAKE_CASE_					)
						UpperCAmelCase_:		List[Any]							      =     self.dummy_vae
						UpperCAmelCase_:		int							      =     self.dummy_text_encoder
						UpperCAmelCase_:		int							      =     XLMRobertaTokenizer.from_pretrained("""hf-internal-testing/tiny-xlm-roberta"""					)
						UpperCAmelCase_:		Tuple							      =     77

						UpperCAmelCase_:		Tuple							      =     self.dummy_image.to(SCREAMING_SNAKE_CASE_					)
						UpperCAmelCase_:		str							      =     init_image / 2 + 0.5

						# make sure here that pndm scheduler skips prk
						UpperCAmelCase_:		List[Any]							      =     AltDiffusionImgaImgPipeline(
						    unet=SCREAMING_SNAKE_CASE_,   scheduler=SCREAMING_SNAKE_CASE_,   vae=SCREAMING_SNAKE_CASE_,   text_encoder=SCREAMING_SNAKE_CASE_,   tokenizer=SCREAMING_SNAKE_CASE_,   safety_checker=SCREAMING_SNAKE_CASE_,   feature_extractor=self.dummy_extractor,   )
						UpperCAmelCase_:		Dict							      =     VaeImageProcessor(vae_scale_factor=alt_pipe.vae_scale_factor,   do_normalize=SCREAMING_SNAKE_CASE_					)
						UpperCAmelCase_:		Optional[int]							      =     alt_pipe.to(SCREAMING_SNAKE_CASE_					)
						alt_pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE_					)

						UpperCAmelCase_:		str							      =     """A painting of a squirrel eating a burger"""
						UpperCAmelCase_:		List[str]							      =     torch.Generator(device=SCREAMING_SNAKE_CASE_					).manual_seed(0					)
						UpperCAmelCase_:		Optional[int]							      =     alt_pipe(
						    [prompt],   generator=SCREAMING_SNAKE_CASE_,   guidance_scale=6.0,   num_inference_steps=2,   output_type="""np""",   image=SCREAMING_SNAKE_CASE_,   )

						UpperCAmelCase_:		List[str]							      =     output.images

						UpperCAmelCase_:		Optional[Any]							      =     torch.Generator(device=SCREAMING_SNAKE_CASE_					).manual_seed(0					)
						UpperCAmelCase_:		Any							      =     alt_pipe(
						    [prompt],   generator=SCREAMING_SNAKE_CASE_,   guidance_scale=6.0,   num_inference_steps=2,   output_type="""np""",   image=SCREAMING_SNAKE_CASE_,   return_dict=SCREAMING_SNAKE_CASE_,   )[0]

						UpperCAmelCase_:		List[str]							      =     image[0, -3:, -3:, -1]
						UpperCAmelCase_:		str							      =     image_from_tuple[0, -3:, -3:, -1]

						assert image.shape == (1, 32, 32, 3)
						UpperCAmelCase_:		Tuple							      =     np.array([0.4_4_2_7, 0.3_7_3_1, 0.4_2_4_9, 0.4_9_4_1, 0.4_5_4_6, 0.4_1_4_8, 0.4_1_9_3, 0.4_6_6_6, 0.4_4_9_9]					)

						assert np.abs(image_slice.flatten() - expected_slice					).max() < 5E-3
						assert np.abs(image_from_tuple_slice.flatten() - expected_slice					).max() < 5E-3

			@unittest.skipIf(torch_device != """cuda""",   """This test requires a GPU"""					)
			def      __snake_case       (self					)       ->   str:
						UpperCAmelCase_:		List[Any]							      =     self.dummy_cond_unet
						UpperCAmelCase_:		List[Any]							      =     PNDMScheduler(skip_prk_steps=SCREAMING_SNAKE_CASE_					)
						UpperCAmelCase_:		Any							      =     self.dummy_vae
						UpperCAmelCase_:		Optional[Any]							      =     self.dummy_text_encoder
						UpperCAmelCase_:		Dict							      =     XLMRobertaTokenizer.from_pretrained("""hf-internal-testing/tiny-xlm-roberta"""					)
						UpperCAmelCase_:		Union[str, Any]							      =     77

						UpperCAmelCase_:		Tuple							      =     self.dummy_image.to(SCREAMING_SNAKE_CASE_					)

						# put models in fp16
						UpperCAmelCase_:		Optional[Any]							      =     unet.half()
						UpperCAmelCase_:		Any							      =     vae.half()
						UpperCAmelCase_:		str							      =     bert.half()

						# make sure here that pndm scheduler skips prk
						UpperCAmelCase_:		Optional[int]							      =     AltDiffusionImgaImgPipeline(
						    unet=SCREAMING_SNAKE_CASE_,   scheduler=SCREAMING_SNAKE_CASE_,   vae=SCREAMING_SNAKE_CASE_,   text_encoder=SCREAMING_SNAKE_CASE_,   tokenizer=SCREAMING_SNAKE_CASE_,   safety_checker=SCREAMING_SNAKE_CASE_,   feature_extractor=self.dummy_extractor,   )
						UpperCAmelCase_:		Optional[Any]							      =     VaeImageProcessor(vae_scale_factor=alt_pipe.vae_scale_factor,   do_normalize=SCREAMING_SNAKE_CASE_					)
						UpperCAmelCase_:		Any							      =     alt_pipe.to(SCREAMING_SNAKE_CASE_					)
						alt_pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE_					)

						UpperCAmelCase_:		Any							      =     """A painting of a squirrel eating a burger"""
						UpperCAmelCase_:		Optional[Any]							      =     torch.manual_seed(0					)
						UpperCAmelCase_:		Optional[int]							      =     alt_pipe(
						    [prompt],   generator=SCREAMING_SNAKE_CASE_,   num_inference_steps=2,   output_type="""np""",   image=SCREAMING_SNAKE_CASE_,   ).images

						assert image.shape == (1, 32, 32, 3)

			@unittest.skipIf(torch_device != """cuda""",   """This test requires a GPU"""					)
			def      __snake_case       (self					)       ->   Tuple:
						UpperCAmelCase_:		Optional[Any]							      =     load_image(
						    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
						    """/img2img/sketch-mountains-input.jpg"""					)
						# resize to resolution that is divisible by 8 but not 16 or 32
						UpperCAmelCase_:		List[Any]							      =     init_image.resize((760, 504)					)

						UpperCAmelCase_:		Tuple							      =     """BAAI/AltDiffusion"""
						UpperCAmelCase_:		Tuple							      =     AltDiffusionImgaImgPipeline.from_pretrained(
						    SCREAMING_SNAKE_CASE_,   safety_checker=SCREAMING_SNAKE_CASE_,   )
						pipe.to(SCREAMING_SNAKE_CASE_					)
						pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE_					)
						pipe.enable_attention_slicing()

						UpperCAmelCase_:		Tuple							      =     """A fantasy landscape, trending on artstation"""

						UpperCAmelCase_:		Optional[int]							      =     torch.manual_seed(0					)
						UpperCAmelCase_:		int							      =     pipe(
						    prompt=SCREAMING_SNAKE_CASE_,   image=SCREAMING_SNAKE_CASE_,   strength=0.7_5,   guidance_scale=7.5,   generator=SCREAMING_SNAKE_CASE_,   output_type="""np""",   )
						UpperCAmelCase_:		Dict							      =     output.images[0]

						UpperCAmelCase_:		Tuple							      =     image[255:258, 383:386, -1]

						assert image.shape == (504, 760, 3)
						UpperCAmelCase_:		str							      =     np.array([0.9_3_5_8, 0.9_3_9_7, 0.9_5_9_9, 0.9_9_0_1, 1.0_0_0_0, 1.0_0_0_0, 0.9_8_8_2, 1.0_0_0_0, 1.0_0_0_0]					)

						assert np.abs(image_slice.flatten() - expected_slice					).max() < 1E-2







@slow
@require_torch_gpu
class 				_a				(   unittest.TestCase					):

			def      __snake_case       (self					)       ->   int:
						# clean up the VRAM after each test
						super().tearDown()
						gc.collect()
						torch.cuda.empty_cache()

			def      __snake_case       (self					)       ->   Union[str, Any]:
						UpperCAmelCase_:		Optional[Any]							      =     load_image(
						    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
						    """/img2img/sketch-mountains-input.jpg"""					)
						UpperCAmelCase_:		Optional[Any]							      =     init_image.resize((768, 512)					)
						UpperCAmelCase_:		Union[str, Any]							      =     load_numpy(
						    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/img2img/fantasy_landscape_alt.npy"""					)

						UpperCAmelCase_:		List[str]							      =     """BAAI/AltDiffusion"""
						UpperCAmelCase_:		int							      =     AltDiffusionImgaImgPipeline.from_pretrained(
						    SCREAMING_SNAKE_CASE_,   safety_checker=SCREAMING_SNAKE_CASE_,   )
						pipe.to(SCREAMING_SNAKE_CASE_					)
						pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE_					)
						pipe.enable_attention_slicing()

						UpperCAmelCase_:		Dict							      =     """A fantasy landscape, trending on artstation"""

						UpperCAmelCase_:		int							      =     torch.manual_seed(0					)
						UpperCAmelCase_:		int							      =     pipe(
						    prompt=SCREAMING_SNAKE_CASE_,   image=SCREAMING_SNAKE_CASE_,   strength=0.7_5,   guidance_scale=7.5,   generator=SCREAMING_SNAKE_CASE_,   output_type="""np""",   )
						UpperCAmelCase_:		Dict							      =     output.images[0]

						assert image.shape == (512, 768, 3)
						# img2img is flaky across GPUs even in fp32, so using MAE here
						assert np.abs(expected_image - image					).max() < 1E-2






